Mozilla is funding a project for bringing Julia to Firefox and the general browser environment

Last week, Mozilla disclosed the winners of Mozilla Research Grants for the first half of 2019. Among the winning proposals was “Bringing Julia to the Browser” that aligns with Mozilla’s goal to bring data science and scientific computing tools to the browser. Mozilla had said it was specifically interested in receiving submissions about supporting R or Julia at the browser level.

Every six months Mozilla awards grants of value $25,000 to support research in emerging technologies and also topics relevant to Mozilla. It started accepting proposals for 2019 H1 funding series in April this year.

I'm happy to announce the 2019H1 @Mozilla Research Grants! We're looking for research to help us keep the Internet safe, open, and accessible to all, as it evolves. We're accepting proposals to answer 12 #research questions, due May 31st. https://t.co/pfqRdNTL3z

Mozilla has been constantly putting its efforts to make the life of data scientists easier on the web. In March, it introduced Iodide that allows data scientists to create interactive documents using web technologies. In April, it came up with Pyodide that brings the Python runtime to the browser via WebAssembly.

By funding this research by Valentin Churavy, an MIT Ph.D. student and a member of the official Julia team, Mozilla is taking the next step towards improving access to popular data science tools on the web. They are planning to port R or Julia, languages that are popular among statisticians and data miners, to WebAssembly. Their ultimate goal is to introduce a plugin for Iodide that will automatically convert data basic types between R/Julia and Javascript and will be able to share class instances between R/Julia and Javascript.

Though Python and R have been developers’ first choice, Julia is also catching up in becoming one of the promising languages for scientific computing. Its execution speeds are comparable to that of C/ C++ and high-level abstractions are comparable to MATLAB. It offers support for modern machine learning frameworks such as TensorFlow and MXNet. Developers can also use Flux, a Julia machine learning library to easily write neural networks.